In Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE : To develop predictive models with dosimetric and clinical variables for temporal lobe injury (TLI) in nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).
MATERIALS AND METHODS : Data of 8194 NPC patients who received IMRT-based treatment were retrospectively reviewed. TLI was diagnosed by magnetic resonance imaging. Dosimetric factors were selected by penalized regression and machine learning, with area under the receiver operating curve (AUC) calculated. Cox proportional hazards models containing the most predictive dosimetric factor with/without clinical variables were performed. A nomogram was generated as a visualization of Cox regression for predicting TLI-free survival.
RESULTS : During median follow-up of 66.8 months (interquartile range [IQR] 54.2-82.2 months), 12.1% of patients (989/8194) developed TLI. Median latency from IMRT to TLI was 36 months (IQR 28-47 months). D0.5cc (dose delivered to 0.5-cm3 temporal-lobe volume) was the most predictive dosimetric factor (AUC: 0.799). Tolerance dose for 5% and 50% probabilities to develop TLI in 5 years were 65.06 Gy (95% confidence interval [CI]: 64.19-65.92) and 89.75 Gy (95% CI: 87.39-92.11), respectively. A nomogram comprising age, T stage, and D0.5cc significantly outperformed the model with only D0.5cc in predicting TLI (C-index: 0.78 vs. 0.737 in train set; 0.775 vs. 0.73 in test set; both P < 0.001). The nomogram-defined high-risk group had worse 5-year TLI-free survival.
CONCLUSIONS : D0.5cc of 65.06 Gy was the tolerance dose of the temporal lobe. Reducing D0.5cc decreased risk of TLI, especially in older patients with advanced T stage. The nomogram could predict TLI precisely and allow individualized follow-up management.
Wen Dan-Wan, Lin Li, Mao Yan-Ping, Chen Chun-Yan, Chen Fo-Ping, Wu Chen-Fei, Huang Xiao-Dan, Li Zhi-Xuan, Xu Si-Si, Kou Jia, Yang Xing-Li, Ma Jun, Sun Ying, Zhou Guan-Qun
Intensity-modulated radiotherapy, Machine learning, Nasopharyngeal carcinoma, Nomogram, Normal tissue complication probability, Temporal lobe injury